parameteruncertainty
Parameter uncertainty refers to the lack of precise knowledge about the value of a model parameter. In frequentist statistics, a parameter is considered fixed but unknown, and the data are random, so sampling variability induces uncertainty about the parameter’s true value. In Bayesian analysis, parameters are treated as random variables with a prior distribution updated to a posterior distribution by the data, which directly expresses uncertainty about the parameter.
Sources of parameter uncertainty include finite sample sizes, measurement error, and model misspecification. Additional factors are
Quantification and communication of parameter uncertainty employ different frameworks. Frequentist methods use standard errors and confidence
The implications of parameter uncertainty extend to predictions and decisions. Higher uncertainty about parameters leads to
See also: parameter identifiability, measurement error, model uncertainty, predictive uncertainty, confidence interval, credible interval, Fisher information,